What Business Data Is Valuable for AI Training?
Watch Jason walk through the three examples, then explore the records and decisions below.
Your everyday work can contain useful AI training data.
An accounting correction. A resolved support ticket. An order delivered despite a stock shortage. Each can leave a record of how people use information, make decisions, and verify their work.
The opportunity starts with recognizing that knowledge in the records your business already keeps.
Four questions reveal the useful history.
Use these questions to examine a completed piece of work. They help you locate the original task, the evidence someone used, and the record of what happened.
- 01 / TASK What needed to get done? The problem, request, or objective.
- 02 / INFORMATION What did they know? The records, tools, policies, and constraints available.
- 03 / DECISIONS What did they do? The investigation, choices, corrections, and approvals.
- 04 / OUTCOME How was it checked? The evidence that the result met the requirements.
The following scenarios are hypothetical teaching examples, not actual data licensing deals or guarantees that a dataset will qualify.
Finding the reason behind a $3,000 difference.
An accountant is reconciling a supplier account. For the same cutoff date, the supplier statement shows $25,000 owed, while the accounts payable ledger shows $28,000.
- What work was someone trying to complete? Reconcile the supplier account and explain the $3,000 difference.
- What information did they have? The supplier statement, original ledger export, invoices, purchase orders, and prior reconciliation.
- What decisions or corrections did they make? The accountant matches transactions and connects two ledger entries to the same original $3,000 invoice. After confirming there is only one liability, they submit a correction for review and approval.
- How do we know the result was acceptable? The approved correction is posted. The updated ledger agrees with the $25,000 statement, each valid invoice appears once, and the reviewer signs off with no unexplained difference.
Potential AI task: Give an AI the starting records and ask it to investigate the discrepancy, cite the supporting invoice, and propose an appropriate correction. A reviewer can assess the evidence and proposed action against the documented case.
A support ticket that shows how the problem was solved.
An IT services company receives a ticket from a customer's employee: email and public websites work, but a required internal application will not open.
- 01 · Reported problem The employee cannot reach the internal application.
- 02 · Diagnostic evidence Service checks show no outage; VPN logs identify an expired device certificate.
- 03 · Authorized correction Support follows the approved certificate renewal procedure and reconnects the VPN.
- 04 · Confirmed access The connection succeeds, the application works, and the employee confirms the result.
- What work was someone trying to complete? Restore the employee's authorized access to the application.
- What information did they have? The ticket conversation, service status, VPN logs, relevant configuration, and troubleshooting instructions.
- What decisions or corrections did they make? Support checks for an outage, investigates the connection failure, and identifies the expired certificate. An authorized technician follows the renewal procedure and reconnects the device.
- How do we know the result was acceptable? The VPN connects successfully, the employee can use the application with their existing permissions, and their confirmation is recorded in the ticket.
Potential AI task: Ask an AI to examine the symptoms and available evidence, identify the likely cause, and recommend the approved next step or an escalation. A ticket marked “closed” becomes more informative when it includes evidence that the issue was resolved.
Meeting a deadline with only part of the order in stock.
A customer needs 120 units within three days. The distributor has 80. The regular supplier's next 40 units will arrive in five days, so the team needs another workable plan.
120 units needed by day three
- What work was someone trying to complete? Fulfill the entire 120-unit order by the customer's deadline.
- What information did they have? The order, inventory records, supplier availability, delivery commitments, quoted costs, and approval policy.
- What decisions or corrections did they make? The team verifies a backup supplier can deliver the missing 40 units on time for an extra $180. A manager approves the cost, the customer agrees to split delivery, and the team arranges both shipments.
- How do we know the result was acceptable? Delivery records confirm that all 120 units arrived by day three. The additional cost matches the approval, and both shipments are linked to the original order.
Potential AI task: Ask an AI to propose a fulfillment plan that respects inventory, timing, cost, and approval requirements. The case provides a way to check whether the plan satisfies all of those constraints.
The connections between records matter.
The full history may be spread across email, spreadsheets, a ticket system, and business software. A case ID, ticket ID, or order number helps connect those records so someone can follow the work.
That is why an email exchange or support ticket can be useful: it may explain how the source documents led to a decision. Attachments, timestamps, and links to final records help preserve that context.
How a data lab can use this history.
A data lab can help organize appropriate records into tasks with a clear objective, relevant evidence and tools, and criteria for judging the result. micro1 describes this approach in its explanation of its flow platform. [1]
For an evaluation, the starting information can be presented to an AI while the historical resolution is kept separate for assessment. Reviewers still need to check that the recorded outcome is sound and allow for other valid solutions.
Each example here illustrates a possible use. Actual preparation, licensing terms, and buyer demand depend on the specific dataset.
Does your business have a history like this?
Identify a few recurring workflows and where their records live. Note how much history you have, which systems connect, and whether the outcomes are documented.
Then visit the data licensing page to explore company fit, privacy considerations, and the introduction process.
Explore data licensing opportunitiesmicro1 currently lists accounting, customer support, and logistics among its areas of interest. It evaluates each company's data individually; these examples do not establish eligibility or a price. Permissions, privacy protections, and permitted uses need to be addressed in the licensing process. [2]
Disclosure: I'm Jason Varner, founder of DataHasValue and a referral partner with micro1. I may receive compensation from qualifying company referrals through links on this website.
